{"url":"/dataset/larc","name":"LARC","full_name":"Language-annotated Abstraction and Reasoning","description_markdown":"**LARC** is a dataset built from ARC (Abstraction and Reasoning Corpus). ARC is a set of tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI.\r\n\r\nLARC or Language-annotated ARC, is a collection of natural language descriptions by a group of human participants, unfamiliar both with ARC and with each other, who instruct each other on how to solve ARC tasks. LARC contains successful instructions for 88% of the ARC tasks.","description_withheld":null,"homepage":"https://github.com/samacqua/LARC","introduced_date":"2021-06-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/communicating-natural-programs-to-humans-and","title":"Communicating Natural Programs to Humans and Machines","first_author":"Samuel Acquaviva","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LARC"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}